Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care

Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care

Abstract The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mech...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Arne Peine, Ahmed Hallawa, Johannes Bickenbach, Guido Dartmann, Lejla Begic Fazlic, Anke Schmeink, Gerd Ascheid, Christoph Thiemermann, Andreas Schuppert, Ryan Kindle, Leo Celi, Gernot Marx, Lukas Martin
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Computer applications to medicine. Medical informatics
R858-859.7
Acceso en línea:https://doaj.org/article/cbf3e67d243d4b4c9450953527ffd35b
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Ejemplares similares